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Publication date: 1 March 1999

Diego P. Ruiz and Antolino Gallego

In this paper we present a new autoregressive (AR) method for bispectrum estimation defined in terms of its third‐moment sequence. The method is based on the segmentation of data…

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Abstract

In this paper we present a new autoregressive (AR) method for bispectrum estimation defined in terms of its third‐moment sequence. The method is based on the segmentation of data into coupled records, and can be considered to be a modification of the “third order recursion method”(TOR). Its foundation resides in considering the data of the process at the left and the right of each record (needed for the calculation of third moment sequence) as not null and taking them as the data corresponding to the preceding and succeeding record respectively. Several simulated examples show that this method allows model parameters to be obtained with greater precision, most of all when only few data are available per record. The influence of factors such as number of data and records, model order, and added white and coloured Gaussian noise on the parameters’ estimation is also considered.

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COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 18 no. 1
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 1 March 2000

Antolino Gallego and Diego P. Ruiz

This paper deals with bispectrum estimation via autoregressive (AR) modelling of a process contaminated by additive Gaussian noise (white and coloured). Two main contributions are…

295

Abstract

This paper deals with bispectrum estimation via autoregressive (AR) modelling of a process contaminated by additive Gaussian noise (white and coloured). Two main contributions are provided in this work. First, a comparison between the existing third order recursion (TOR) and the constrained third order mean (CTOM) methods is presented. Basically, the second method is shown to be a smoothing windowed version (i.e. a covariance‐type estimator) of the first one, achieved at the expense of the loss of the recursivity in the AR‐model order. This prior analysis has induced us to develop an alternative scheme to tackle this type of problem, which, while maintaining the main feature of the CTOM method as a covariance type estimator, is a recursive‐in‐order algorithm. This recursivity is obtained carrying out an appropriate minimization procedure of some prediction squared errors also defined here. The paper also compares, by means of simulations, this proposed method and the two existing ones.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 19 no. 1
Type: Research Article
ISSN: 0332-1649

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